Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations199
Missing cells730
Missing cells (%)16.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory132.7 KiB
Average record size in memory683.0 B

Variable types

Text2
DateTime1
Categorical11
Numeric8

Alerts

cluster_k_4 has constant value "2" Constant
Estado is highly overall correlated with cant_MontoLimiteHigh correlation
anio_preinscripcion is highly overall correlated with antiguedadHigh correlation
antiguedad is highly overall correlated with anio_preinscripcionHigh correlation
cant_Apoderado is highly overall correlated with cant_MontoLimite and 2 other fieldsHigh correlation
cant_MontoLimite is highly overall correlated with Estado and 7 other fieldsHigh correlation
cant_antecedentes is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
cant_apercibimientos is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
cant_autenticado is highly overall correlated with cant_sinMontoLimiteHigh correlation
cant_noAutenticado is highly overall correlated with cant_Apoderado and 3 other fieldsHigh correlation
cant_procesos_adjudicado is highly overall correlated with monto_total_adjudicadoHigh correlation
cant_representante is highly overall correlated with cant_MontoLimiteHigh correlation
cant_sinMontoLimite is highly overall correlated with cant_Apoderado and 2 other fieldsHigh correlation
cant_suspensiones is highly overall correlated with cant_MontoLimite and 2 other fieldsHigh correlation
monto_total_adjudicado is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
total_articulos_provee is highly overall correlated with cant_MontoLimiteHigh correlation
Estado is highly imbalanced (71.1%) Imbalance
provincia is highly imbalanced (51.8%) Imbalance
cant_apercibimientos is highly imbalanced (74.6%) Imbalance
cant_representante is highly imbalanced (58.5%) Imbalance
cant_autenticado is highly imbalanced (66.2%) Imbalance
cant_noAutenticado is highly imbalanced (50.8%) Imbalance
cant_socios has 45 (22.6%) missing values Missing
cant_apercibimientos has 58 (29.1%) missing values Missing
cant_suspensiones has 109 (54.8%) missing values Missing
cant_antecedentes has 3 (1.5%) missing values Missing
cant_Apoderado has 56 (28.1%) missing values Missing
cant_representante has 104 (52.3%) missing values Missing
cant_noAutenticado has 159 (79.9%) missing values Missing
cant_MontoLimite has 193 (97.0%) missing values Missing
CUIT has unique values Unique

Reproduction

Analysis started2025-06-24 13:32:42.087666
Analysis finished2025-06-24 13:32:49.238258
Duration7.15 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
2025-06-24T10:32:49.352629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.994975
Min length9

Characters and Unicode

Total characters2188
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique199 ?
Unique (%)100.0%

Sample

1st row30711500363
2nd row30590151013
3rd row30678561165
4th row30591267759
5th row30702024834
ValueCountFrequency (%)
30711500363 1
 
0.5%
30590151013 1
 
0.5%
30678561165 1
 
0.5%
30591267759 1
 
0.5%
30702024834 1
 
0.5%
30623295946 1
 
0.5%
30673249902 1
 
0.5%
30714236888 1
 
0.5%
30710362218 1
 
0.5%
30710828608 1
 
0.5%
Other values (189) 189
95.0%
2025-06-24T10:32:49.523222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 358
16.4%
3 307
14.0%
7 251
11.5%
2 240
11.0%
1 202
9.2%
6 198
9.0%
9 172
7.9%
8 163
7.4%
5 158
7.2%
4 138
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 358
16.4%
3 307
14.0%
7 251
11.5%
2 240
11.0%
1 202
9.2%
6 198
9.0%
9 172
7.9%
8 163
7.4%
5 158
7.2%
4 138
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 358
16.4%
3 307
14.0%
7 251
11.5%
2 240
11.0%
1 202
9.2%
6 198
9.0%
9 172
7.9%
8 163
7.4%
5 158
7.2%
4 138
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 358
16.4%
3 307
14.0%
7 251
11.5%
2 240
11.0%
1 202
9.2%
6 198
9.0%
9 172
7.9%
8 163
7.4%
5 158
7.2%
4 138
 
6.3%

Nombre
Text

Distinct194
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
2025-06-24T10:32:49.721214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length67
Median length34
Mean length18.507538
Min length3

Characters and Unicode

Total characters3683
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique193 ?
Unique (%)97.0%

Sample

1st rowLICICOM S.R.L.
2nd rowVIDITEC S.A..
3rd rowNACION SEGUROS S.A.
4th rowERRE-DE SRL
5th rowDatastar Argentina S.A.
ValueCountFrequency (%)
s.a 59
 
10.4%
srl 36
 
6.3%
s.r.l 21
 
3.7%
sa 20
 
3.5%
argentina 13
 
2.3%
y 11
 
1.9%
de 10
 
1.8%
datos 6
 
1.1%
sin 6
 
1.1%
s 5
 
0.9%
Other values (341) 380
67.0%
2025-06-24T10:32:50.025666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
368
 
10.0%
A 292
 
7.9%
S 269
 
7.3%
R 228
 
6.2%
E 201
 
5.5%
I 200
 
5.4%
. 190
 
5.2%
N 146
 
4.0%
O 141
 
3.8%
L 135
 
3.7%
Other values (54) 1513
41.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3683
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
368
 
10.0%
A 292
 
7.9%
S 269
 
7.3%
R 228
 
6.2%
E 201
 
5.5%
I 200
 
5.4%
. 190
 
5.2%
N 146
 
4.0%
O 141
 
3.8%
L 135
 
3.7%
Other values (54) 1513
41.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3683
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
368
 
10.0%
A 292
 
7.9%
S 269
 
7.3%
R 228
 
6.2%
E 201
 
5.5%
I 200
 
5.4%
. 190
 
5.2%
N 146
 
4.0%
O 141
 
3.8%
L 135
 
3.7%
Other values (54) 1513
41.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3683
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
368
 
10.0%
A 292
 
7.9%
S 269
 
7.3%
R 228
 
6.2%
E 201
 
5.5%
I 200
 
5.4%
. 190
 
5.2%
N 146
 
4.0%
O 141
 
3.8%
L 135
 
3.7%
Other values (54) 1513
41.1%
Distinct156
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum2016-02-08 00:00:00
Maximum2020-10-16 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-24T10:32:50.117350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:50.235070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estado
Categorical

High correlation  Imbalance 

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Inscripto
174 
Desactualizado Por Documentos Vencidos
 
7
Suspendido
 
5
Con Solicitud De Baja
 
4
Desactualizado Por Mantencion Formulario
 
4
Other values (3)
 
5

Length

Max length40
Median length9
Mean length11.065327
Min length9

Characters and Unicode

Total characters2202
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowInscripto
2nd rowInscripto
3rd rowInscripto
4th rowInscripto
5th rowInscripto

Common Values

ValueCountFrequency (%)
Inscripto 174
87.4%
Desactualizado Por Documentos Vencidos 7
 
3.5%
Suspendido 5
 
2.5%
Con Solicitud De Baja 4
 
2.0%
Desactualizado Por Mantencion Formulario 4
 
2.0%
Pre Inscripto 3
 
1.5%
En Evaluacion 1
 
0.5%
Desactualizado Por Clase 1
 
0.5%

Length

2025-06-24T10:32:50.340109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:32:50.413894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 177
70.8%
desactualizado 12
 
4.8%
por 12
 
4.8%
documentos 7
 
2.8%
vencidos 7
 
2.8%
suspendido 5
 
2.0%
con 4
 
1.6%
solicitud 4
 
1.6%
de 4
 
1.6%
baja 4
 
1.6%
Other values (6) 14
 
5.6%

Most occurring characters

ValueCountFrequency (%)
o 248
11.3%
i 218
9.9%
n 214
9.7%
c 212
9.6%
s 209
9.5%
t 204
9.3%
r 200
9.1%
p 182
8.3%
I 177
8.0%
a 55
 
2.5%
Other values (18) 283
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 248
11.3%
i 218
9.9%
n 214
9.7%
c 212
9.6%
s 209
9.5%
t 204
9.3%
r 200
9.1%
p 182
8.3%
I 177
8.0%
a 55
 
2.5%
Other values (18) 283
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 248
11.3%
i 218
9.9%
n 214
9.7%
c 212
9.6%
s 209
9.5%
t 204
9.3%
r 200
9.1%
p 182
8.3%
I 177
8.0%
a 55
 
2.5%
Other values (18) 283
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 248
11.3%
i 218
9.9%
n 214
9.7%
c 212
9.6%
s 209
9.5%
t 204
9.3%
r 200
9.1%
p 182
8.3%
I 177
8.0%
a 55
 
2.5%
Other values (18) 283
12.9%

TipoSocietario
Categorical

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
Sociedad Anónima
82 
Sociedad Responsabilidad Limitada
60 
Persona Física
44 
Otras Formas Societarias
 
7
Sociedades De Hecho
 
3
Other values (2)
 
3

Length

Max length40
Median length33
Mean length21.256281
Min length14

Characters and Unicode

Total characters4230
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowSociedad Responsabilidad Limitada
2nd rowSociedad Anónima
3rd rowSociedad Anónima
4th rowSociedad Responsabilidad Limitada
5th rowSociedad Anónima

Common Values

ValueCountFrequency (%)
Sociedad Anónima 82
41.2%
Sociedad Responsabilidad Limitada 60
30.2%
Persona Física 44
22.1%
Otras Formas Societarias 7
 
3.5%
Sociedades De Hecho 3
 
1.5%
Persona Jurídica Extranjero Sin Sucursal 2
 
1.0%
Organismo Publico 1
 
0.5%

Length

2025-06-24T10:32:50.516371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:32:50.581622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sociedad 142
30.0%
anónima 82
17.3%
responsabilidad 60
12.7%
limitada 60
12.7%
persona 46
 
9.7%
física 44
 
9.3%
otras 7
 
1.5%
formas 7
 
1.5%
societarias 7
 
1.5%
sociedades 3
 
0.6%
Other values (8) 16
 
3.4%

Most occurring characters

ValueCountFrequency (%)
a 592
14.0%
i 531
12.6%
d 472
11.2%
n 275
 
6.5%
275
 
6.5%
o 272
 
6.4%
e 269
 
6.4%
s 237
 
5.6%
c 204
 
4.8%
S 156
 
3.7%
Other values (23) 947
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 592
14.0%
i 531
12.6%
d 472
11.2%
n 275
 
6.5%
275
 
6.5%
o 272
 
6.4%
e 269
 
6.4%
s 237
 
5.6%
c 204
 
4.8%
S 156
 
3.7%
Other values (23) 947
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 592
14.0%
i 531
12.6%
d 472
11.2%
n 275
 
6.5%
275
 
6.5%
o 272
 
6.4%
e 269
 
6.4%
s 237
 
5.6%
c 204
 
4.8%
S 156
 
3.7%
Other values (23) 947
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 592
14.0%
i 531
12.6%
d 472
11.2%
n 275
 
6.5%
275
 
6.5%
o 272
 
6.4%
e 269
 
6.4%
s 237
 
5.6%
c 204
 
4.8%
S 156
 
3.7%
Other values (23) 947
22.4%

anio_preinscripcion
Categorical

High correlation 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2016
84 
2017
82 
2018
22 
2019
 
8
2020
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters796
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 84
42.2%
2017 82
41.2%
2018 22
 
11.1%
2019 8
 
4.0%
2020 3
 
1.5%

Length

2025-06-24T10:32:50.686773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:32:50.738840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2016 84
42.2%
2017 82
41.2%
2018 22
 
11.1%
2019 8
 
4.0%
2020 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
2 202
25.4%
0 202
25.4%
1 196
24.6%
6 84
10.6%
7 82
10.3%
8 22
 
2.8%
9 8
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 796
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 202
25.4%
0 202
25.4%
1 196
24.6%
6 84
10.6%
7 82
10.3%
8 22
 
2.8%
9 8
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 796
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 202
25.4%
0 202
25.4%
1 196
24.6%
6 84
10.6%
7 82
10.3%
8 22
 
2.8%
9 8
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 796
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 202
25.4%
0 202
25.4%
1 196
24.6%
6 84
10.6%
7 82
10.3%
8 22
 
2.8%
9 8
 
1.0%

cant_procesos_adjudicado
Real number (ℝ)

High correlation 

Distinct106
Distinct (%)53.5%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean112.86364
Minimum1
Maximum1214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-06-24T10:32:50.835253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median30
Q3111.75
95-th percentile578.9
Maximum1214
Range1213
Interquartile range (IQR)103.75

Descriptive statistics

Standard deviation208.79209
Coefficient of variation (CV)1.8499501
Kurtosis10.14718
Mean112.86364
Median Absolute Deviation (MAD)27
Skewness3.0988858
Sum22347
Variance43594.139
MonotonicityNot monotonic
2025-06-24T10:32:50.947457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 14
 
7.0%
1 9
 
4.5%
2 9
 
4.5%
5 7
 
3.5%
22 7
 
3.5%
13 6
 
3.0%
6 4
 
2.0%
8 4
 
2.0%
14 4
 
2.0%
44 3
 
1.5%
Other values (96) 131
65.8%
ValueCountFrequency (%)
1 9
4.5%
2 9
4.5%
3 14
7.0%
4 3
 
1.5%
5 7
3.5%
6 4
 
2.0%
7 3
 
1.5%
8 4
 
2.0%
10 2
 
1.0%
11 2
 
1.0%
ValueCountFrequency (%)
1214 1
0.5%
1102 1
0.5%
989 1
0.5%
895 1
0.5%
889 1
0.5%
864 1
0.5%
804 1
0.5%
792 1
0.5%
649 1
0.5%
635 1
0.5%

monto_total_adjudicado
Real number (ℝ)

High correlation 

Distinct198
Distinct (%)100.0%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.551944 × 108
Minimum6872
Maximum6.9338191 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-06-24T10:32:51.051150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6872
5-th percentile546768.44
Q17498428.6
median35763034
Q31.9150669 × 108
95-th percentile8.0486084 × 108
Maximum6.9338191 × 109
Range6.9338122 × 109
Interquartile range (IQR)1.8400826 × 108

Descriptive statistics

Standard deviation7.6527844 × 108
Coefficient of variation (CV)2.9988058
Kurtosis45.732919
Mean2.551944 × 108
Median Absolute Deviation (MAD)34525936
Skewness6.2881464
Sum5.0528491 × 1010
Variance5.8565109 × 1017
MonotonicityNot monotonic
2025-06-24T10:32:51.154405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7425067.382 1
 
0.5%
27146231.22 1
 
0.5%
6933819091 1
 
0.5%
137988939 1
 
0.5%
3182253860 1
 
0.5%
7831100.398 1
 
0.5%
1375720032 1
 
0.5%
7242920.713 1
 
0.5%
86070142.1 1
 
0.5%
64475958.4 1
 
0.5%
Other values (188) 188
94.5%
ValueCountFrequency (%)
6872 1
0.5%
23910.30243 1
0.5%
42000 1
0.5%
84943.48 1
0.5%
100200 1
0.5%
213056.6512 1
0.5%
240550.6182 1
0.5%
386494.5 1
0.5%
508479.1785 1
0.5%
533050.5125 1
0.5%
ValueCountFrequency (%)
6933819091 1
0.5%
5903229493 1
0.5%
3182253860 1
0.5%
3132430594 1
0.5%
2548401882 1
0.5%
2252733473 1
0.5%
1375720032 1
0.5%
1375349531 1
0.5%
1005022056 1
0.5%
943321354.4 1
0.5%

antiguedad
Categorical

High correlation 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
5.0
84 
4.0
82 
3.0
22 
2.0
 
8
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters597
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 84
42.2%
4.0 82
41.2%
3.0 22
 
11.1%
2.0 8
 
4.0%
1.0 3
 
1.5%

Length

2025-06-24T10:32:51.250063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:32:51.313325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5.0 84
42.2%
4.0 82
41.2%
3.0 22
 
11.1%
2.0 8
 
4.0%
1.0 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
. 199
33.3%
0 199
33.3%
5 84
14.1%
4 82
13.7%
3 22
 
3.7%
2 8
 
1.3%
1 3
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 597
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 199
33.3%
0 199
33.3%
5 84
14.1%
4 82
13.7%
3 22
 
3.7%
2 8
 
1.3%
1 3
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 597
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 199
33.3%
0 199
33.3%
5 84
14.1%
4 82
13.7%
3 22
 
3.7%
2 8
 
1.3%
1 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 597
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 199
33.3%
0 199
33.3%
5 84
14.1%
4 82
13.7%
3 22
 
3.7%
2 8
 
1.3%
1 3
 
0.5%

provincia
Categorical

Imbalance 

Distinct13
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
Ciudad Autónoma de Buenos Aires
117 
Buenos Aires
54 
Santa Fe
 
7
Córdoba
 
7
San Juan
 
3
Other values (8)
 
11

Length

Max length31
Median length31
Mean length22.61809
Min length7

Characters and Unicode

Total characters4501
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)2.5%

Sample

1st rowCiudad Autónoma de Buenos Aires
2nd rowCiudad Autónoma de Buenos Aires
3rd rowCiudad Autónoma de Buenos Aires
4th rowBuenos Aires
5th rowCiudad Autónoma de Buenos Aires

Common Values

ValueCountFrequency (%)
Ciudad Autónoma de Buenos Aires 117
58.8%
Buenos Aires 54
27.1%
Santa Fe 7
 
3.5%
Córdoba 7
 
3.5%
San Juan 3
 
1.5%
Corrientes 2
 
1.0%
San Luis 2
 
1.0%
Extranjera 2
 
1.0%
Rio Negro 1
 
0.5%
La Rioja 1
 
0.5%
Other values (3) 3
 
1.5%

Length

2025-06-24T10:32:51.394960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aires 171
23.2%
buenos 171
23.2%
ciudad 117
15.9%
de 117
15.9%
autónoma 117
15.9%
santa 7
 
1.0%
fe 7
 
1.0%
córdoba 7
 
1.0%
san 5
 
0.7%
juan 3
 
0.4%
Other values (11) 14
 
1.9%

Most occurring characters

ValueCountFrequency (%)
537
11.9%
e 475
10.6%
u 412
9.2%
d 359
 
8.0%
s 347
 
7.7%
n 310
 
6.9%
o 302
 
6.7%
i 295
 
6.6%
A 288
 
6.4%
a 270
 
6.0%
Other values (22) 906
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4501
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
537
11.9%
e 475
10.6%
u 412
9.2%
d 359
 
8.0%
s 347
 
7.7%
n 310
 
6.9%
o 302
 
6.7%
i 295
 
6.6%
A 288
 
6.4%
a 270
 
6.0%
Other values (22) 906
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4501
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
537
11.9%
e 475
10.6%
u 412
9.2%
d 359
 
8.0%
s 347
 
7.7%
n 310
 
6.9%
o 302
 
6.7%
i 295
 
6.6%
A 288
 
6.4%
a 270
 
6.0%
Other values (22) 906
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4501
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
537
11.9%
e 475
10.6%
u 412
9.2%
d 359
 
8.0%
s 347
 
7.7%
n 310
 
6.9%
o 302
 
6.7%
i 295
 
6.6%
A 288
 
6.4%
a 270
 
6.0%
Other values (22) 906
20.1%

cant_socios
Real number (ℝ)

Missing 

Distinct6
Distinct (%)3.9%
Missing45
Missing (%)22.6%
Infinite0
Infinite (%)0.0%
Mean1.9350649
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-06-24T10:32:51.488750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0269272
Coefficient of variation (CV)0.53069391
Kurtosis7.9885784
Mean1.9350649
Median Absolute Deviation (MAD)1
Skewness2.1124298
Sum298
Variance1.0545794
MonotonicityNot monotonic
2025-06-24T10:32:51.541413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 72
36.2%
1 55
27.6%
3 16
 
8.0%
4 7
 
3.5%
5 3
 
1.5%
8 1
 
0.5%
(Missing) 45
22.6%
ValueCountFrequency (%)
1 55
27.6%
2 72
36.2%
3 16
 
8.0%
4 7
 
3.5%
5 3
 
1.5%
8 1
 
0.5%
ValueCountFrequency (%)
8 1
 
0.5%
5 3
 
1.5%
4 7
 
3.5%
3 16
 
8.0%
2 72
36.2%
1 55
27.6%

cant_apercibimientos
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)2.1%
Missing58
Missing (%)29.1%
Memory size13.0 KiB
1.0
131 
2.0
 
9
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters423
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 131
65.8%
2.0 9
 
4.5%
3.0 1
 
0.5%
(Missing) 58
29.1%

Length

2025-06-24T10:32:51.628631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:32:51.687117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 131
92.9%
2.0 9
 
6.4%
3.0 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
. 141
33.3%
0 141
33.3%
1 131
31.0%
2 9
 
2.1%
3 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 423
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 141
33.3%
0 141
33.3%
1 131
31.0%
2 9
 
2.1%
3 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 423
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 141
33.3%
0 141
33.3%
1 131
31.0%
2 9
 
2.1%
3 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 423
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 141
33.3%
0 141
33.3%
1 131
31.0%
2 9
 
2.1%
3 1
 
0.2%

cant_suspensiones
Real number (ℝ)

High correlation  Missing 

Distinct7
Distinct (%)7.8%
Missing109
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean2.0555556
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-06-24T10:32:51.733232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1549708
Coefficient of variation (CV)0.56187769
Kurtosis5.4527584
Mean2.0555556
Median Absolute Deviation (MAD)0
Skewness2.0824203
Sum185
Variance1.3339576
MonotonicityNot monotonic
2025-06-24T10:32:51.796603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 48
24.1%
1 27
 
13.6%
3 6
 
3.0%
4 5
 
2.5%
6 2
 
1.0%
5 1
 
0.5%
7 1
 
0.5%
(Missing) 109
54.8%
ValueCountFrequency (%)
1 27
13.6%
2 48
24.1%
3 6
 
3.0%
4 5
 
2.5%
5 1
 
0.5%
6 2
 
1.0%
7 1
 
0.5%
ValueCountFrequency (%)
7 1
 
0.5%
6 2
 
1.0%
5 1
 
0.5%
4 5
 
2.5%
3 6
 
3.0%
2 48
24.1%
1 27
13.6%

cant_antecedentes
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)4.1%
Missing3
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean1.8163265
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-06-24T10:32:51.849194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2139948
Coefficient of variation (CV)0.66837916
Kurtosis6.1345897
Mean1.8163265
Median Absolute Deviation (MAD)0
Skewness2.2516937
Sum356
Variance1.4737834
MonotonicityNot monotonic
2025-06-24T10:32:51.922637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 102
51.3%
2 62
31.2%
3 15
 
7.5%
4 8
 
4.0%
5 4
 
2.0%
6 3
 
1.5%
7 1
 
0.5%
8 1
 
0.5%
(Missing) 3
 
1.5%
ValueCountFrequency (%)
1 102
51.3%
2 62
31.2%
3 15
 
7.5%
4 8
 
4.0%
5 4
 
2.0%
6 3
 
1.5%
7 1
 
0.5%
8 1
 
0.5%
ValueCountFrequency (%)
8 1
 
0.5%
7 1
 
0.5%
6 3
 
1.5%
5 4
 
2.0%
4 8
 
4.0%
3 15
 
7.5%
2 62
31.2%
1 102
51.3%

cant_Apoderado
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)4.2%
Missing56
Missing (%)28.1%
Infinite0
Infinite (%)0.0%
Mean1.3146853
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-06-24T10:32:52.201616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.88349164
Coefficient of variation (CV)0.67201757
Kurtosis25.573298
Mean1.3146853
Median Absolute Deviation (MAD)0
Skewness4.4316866
Sum188
Variance0.78055747
MonotonicityNot monotonic
2025-06-24T10:32:52.271040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 117
58.8%
2 17
 
8.5%
3 4
 
2.0%
4 3
 
1.5%
8 1
 
0.5%
5 1
 
0.5%
(Missing) 56
28.1%
ValueCountFrequency (%)
1 117
58.8%
2 17
 
8.5%
3 4
 
2.0%
4 3
 
1.5%
5 1
 
0.5%
8 1
 
0.5%
ValueCountFrequency (%)
8 1
 
0.5%
5 1
 
0.5%
4 3
 
1.5%
3 4
 
2.0%
2 17
 
8.5%
1 117
58.8%

cant_representante
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)3.2%
Missing104
Missing (%)52.3%
Memory size12.8 KiB
1.0
81 
2.0
13 
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters285
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 81
40.7%
2.0 13
 
6.5%
3.0 1
 
0.5%
(Missing) 104
52.3%

Length

2025-06-24T10:32:52.352309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:32:52.403683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 81
85.3%
2.0 13
 
13.7%
3.0 1
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 95
33.3%
0 95
33.3%
1 81
28.4%
2 13
 
4.6%
3 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 285
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 95
33.3%
0 95
33.3%
1 81
28.4%
2 13
 
4.6%
3 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 285
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 95
33.3%
0 95
33.3%
1 81
28.4%
2 13
 
4.6%
3 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 285
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 95
33.3%
0 95
33.3%
1 81
28.4%
2 13
 
4.6%
3 1
 
0.4%

cant_autenticado
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
1.0
166 
2.0
28 
3.0
 
3
4.0
 
1
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters597
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 166
83.4%
2.0 28
 
14.1%
3.0 3
 
1.5%
4.0 1
 
0.5%
5.0 1
 
0.5%

Length

2025-06-24T10:32:52.462864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:32:52.516094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 166
83.4%
2.0 28
 
14.1%
3.0 3
 
1.5%
4.0 1
 
0.5%
5.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
. 199
33.3%
0 199
33.3%
1 166
27.8%
2 28
 
4.7%
3 3
 
0.5%
4 1
 
0.2%
5 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 597
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 199
33.3%
0 199
33.3%
1 166
27.8%
2 28
 
4.7%
3 3
 
0.5%
4 1
 
0.2%
5 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 597
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 199
33.3%
0 199
33.3%
1 166
27.8%
2 28
 
4.7%
3 3
 
0.5%
4 1
 
0.2%
5 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 597
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 199
33.3%
0 199
33.3%
1 166
27.8%
2 28
 
4.7%
3 3
 
0.5%
4 1
 
0.2%
5 1
 
0.2%

cant_noAutenticado
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)12.5%
Missing159
Missing (%)79.9%
Memory size12.6 KiB
1.0
31 
2.0
3.0
 
2
7.0
 
1
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters120
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)5.0%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 31
 
15.6%
2.0 5
 
2.5%
3.0 2
 
1.0%
7.0 1
 
0.5%
4.0 1
 
0.5%
(Missing) 159
79.9%

Length

2025-06-24T10:32:52.588167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:32:52.640575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 31
77.5%
2.0 5
 
12.5%
3.0 2
 
5.0%
7.0 1
 
2.5%
4.0 1
 
2.5%

Most occurring characters

ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
1 31
25.8%
2 5
 
4.2%
3 2
 
1.7%
7 1
 
0.8%
4 1
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
1 31
25.8%
2 5
 
4.2%
3 2
 
1.7%
7 1
 
0.8%
4 1
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
1 31
25.8%
2 5
 
4.2%
3 2
 
1.7%
7 1
 
0.8%
4 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
1 31
25.8%
2 5
 
4.2%
3 2
 
1.7%
7 1
 
0.8%
4 1
 
0.8%

cant_sinMontoLimite
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)3.0%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.469697
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-06-24T10:32:52.703400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92708314
Coefficient of variation (CV)0.63079884
Kurtosis14.896193
Mean1.469697
Median Absolute Deviation (MAD)0
Skewness3.2557411
Sum291
Variance0.85948316
MonotonicityNot monotonic
2025-06-24T10:32:52.770975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 136
68.3%
2 47
 
23.6%
3 6
 
3.0%
4 5
 
2.5%
5 3
 
1.5%
8 1
 
0.5%
(Missing) 1
 
0.5%
ValueCountFrequency (%)
1 136
68.3%
2 47
 
23.6%
3 6
 
3.0%
4 5
 
2.5%
5 3
 
1.5%
8 1
 
0.5%
ValueCountFrequency (%)
8 1
 
0.5%
5 3
 
1.5%
4 5
 
2.5%
3 6
 
3.0%
2 47
 
23.6%
1 136
68.3%

cant_MontoLimite
Categorical

High correlation  Missing 

Distinct2
Distinct (%)33.3%
Missing193
Missing (%)97.0%
Memory size12.5 KiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)16.7%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5
 
2.5%
2.0 1
 
0.5%
(Missing) 193
97.0%

Length

2025-06-24T10:32:52.850111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:32:52.902547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5
83.3%
2.0 1
 
16.7%

Most occurring characters

ValueCountFrequency (%)
. 6
33.3%
0 6
33.3%
1 5
27.8%
2 1
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 6
33.3%
0 6
33.3%
1 5
27.8%
2 1
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 6
33.3%
0 6
33.3%
1 5
27.8%
2 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 6
33.3%
0 6
33.3%
1 5
27.8%
2 1
 
5.6%

total_articulos_provee
Real number (ℝ)

High correlation 

Distinct147
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean412.94975
Minimum1
Maximum6661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-06-24T10:32:52.978640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q125
median76
Q3325
95-th percentile2062.9
Maximum6661
Range6660
Interquartile range (IQR)300

Descriptive statistics

Standard deviation978.99978
Coefficient of variation (CV)2.370748
Kurtosis19.46077
Mean412.94975
Median Absolute Deviation (MAD)67
Skewness4.1968645
Sum82177
Variance958440.56
MonotonicityNot monotonic
2025-06-24T10:32:53.085104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7
 
3.5%
31 5
 
2.5%
5 5
 
2.5%
47 4
 
2.0%
3 4
 
2.0%
105 3
 
1.5%
30 3
 
1.5%
9 3
 
1.5%
16 3
 
1.5%
46 3
 
1.5%
Other values (137) 159
79.9%
ValueCountFrequency (%)
1 7
3.5%
2 1
 
0.5%
3 4
2.0%
4 2
 
1.0%
5 5
2.5%
6 3
1.5%
7 2
 
1.0%
8 1
 
0.5%
9 3
1.5%
10 1
 
0.5%
ValueCountFrequency (%)
6661 1
0.5%
6064 1
0.5%
5765 1
0.5%
3956 1
0.5%
3686 1
0.5%
3605 1
0.5%
3387 1
0.5%
3210 1
0.5%
3109 1
0.5%
2566 1
0.5%

cluster_k_4
Categorical

Constant 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
2
199 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters199
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 199
100.0%

Length

2025-06-24T10:32:53.184106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:32:53.226774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 199
100.0%

Most occurring characters

ValueCountFrequency (%)
2 199
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 199
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 199
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 199
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 199
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 199
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 199
100.0%

Interactions

2025-06-24T10:32:48.019997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:43.327937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:43.986670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:44.653239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:45.286758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:45.904241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:46.536639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:47.378967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:48.104929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:43.416289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:44.070032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:44.739193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:45.362559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:45.986623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:46.619948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:47.453335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:48.186339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:43.504059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:44.160613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:44.802584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:45.436764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:46.070415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:46.686795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:47.536645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:48.272237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:43.592687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:44.244753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:44.895588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:45.529307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:46.145230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:46.777580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:47.620065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:48.345910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:43.662848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:44.319905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:44.978243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:45.604511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:46.220076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:46.856847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:47.702734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:48.436757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:43.752871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:44.407428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:45.054394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:45.686745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:46.286759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:47.109558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:47.788315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:48.505371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:43.832665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:44.486801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:45.137987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:45.755374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:46.370195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:47.193410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:47.862097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:48.586766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:43.911778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:44.561368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:45.204112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:45.836653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:46.455868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:47.289017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:32:47.936643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-24T10:32:53.279176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EstadoTipoSocietarioanio_preinscripcionantiguedadcant_Apoderadocant_MontoLimitecant_antecedentescant_apercibimientoscant_autenticadocant_noAutenticadocant_procesos_adjudicadocant_representantecant_sinMontoLimitecant_socioscant_suspensionesmonto_total_adjudicadoprovinciatotal_articulos_provee
Estado1.0000.3630.1760.1760.0001.0000.0240.0000.2300.0000.0000.0000.0000.0000.0000.0000.3540.000
TipoSocietario0.3631.0000.3110.3110.0000.0000.0620.0000.0000.0000.0000.0710.0530.0820.0000.4310.3900.000
anio_preinscripcion0.1760.3111.0001.0000.0000.0000.0690.0000.0000.0000.0000.0000.0000.0000.0000.0000.3270.000
antiguedad0.1760.3111.0001.0000.0000.0000.0690.0000.0000.0000.0000.0000.0000.0000.0000.0000.3270.000
cant_Apoderado0.0000.0000.0000.0001.0000.816-0.0700.0000.4590.714-0.0710.4160.693-0.003-0.0610.0360.000-0.186
cant_MontoLimite1.0000.0000.0000.0000.8161.0000.8661.0000.0000.0000.0001.0000.0000.0001.0001.0000.0001.000
cant_antecedentes0.0240.0620.0690.069-0.0700.8661.0000.3340.0140.000-0.0360.238-0.118-0.1790.667-0.1680.2760.158
cant_apercibimientos0.0000.0000.0000.0000.0001.0000.3341.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.171
cant_autenticado0.2300.0000.0000.0000.4590.0000.0140.0001.0000.0000.0000.4980.5470.0000.0000.0000.1860.000
cant_noAutenticado0.0000.0000.0000.0000.7140.0000.0001.0000.0001.0000.0000.0000.6780.4950.8820.3640.0000.000
cant_procesos_adjudicado0.0000.0000.0000.000-0.0710.000-0.0360.0000.0000.0001.0000.0000.0400.120-0.3640.6650.0000.423
cant_representante0.0000.0710.0000.0000.4161.0000.2380.0000.4980.0000.0001.0000.4120.1440.4910.0000.0000.093
cant_sinMontoLimite0.0000.0530.0000.0000.6930.000-0.1180.0000.5470.6780.0400.4121.0000.1420.0370.1350.000-0.144
cant_socios0.0000.0820.0000.000-0.0030.000-0.1790.0000.0000.4950.1200.1440.1421.000-0.2670.2840.0000.044
cant_suspensiones0.0000.0000.0000.000-0.0611.0000.6670.0000.0000.882-0.3640.4910.037-0.2671.000-0.3970.3510.034
monto_total_adjudicado0.0000.4310.0000.0000.0361.000-0.1680.0000.0000.3640.6650.0000.1350.284-0.3971.0000.0000.181
provincia0.3540.3900.3270.3270.0000.0000.2760.0000.1860.0000.0000.0000.0000.0000.3510.0001.0000.000
total_articulos_provee0.0000.0000.0000.000-0.1861.0000.1580.1710.0000.0000.4230.093-0.1440.0440.0340.1810.0001.000

Missing values

2025-06-24T10:32:48.745371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-24T10:32:48.921368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-24T10:32:49.107011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveecluster_k_4
230711500363LICICOM S.R.L.15/09/2016InscriptoSociedad Responsabilidad Limitada201622.07.425067e+065.0Ciudad Autónoma de Buenos Aires2.01.02.04.02.01.02.01.03.0NaN105.02
2330590151013VIDITEC S.A..22/07/2016InscriptoSociedad Anónima201647.02.714623e+075.0Ciudad Autónoma de Buenos Aires5.01.0NaN1.01.0NaN1.0NaN1.0NaN113.02
2430678561165NACION SEGUROS S.A.15/11/2016InscriptoSociedad Anónima20161102.06.933819e+095.0Ciudad Autónoma de Buenos Aires5.0NaNNaNNaN1.0NaN1.0NaN1.0NaN26.02
2930591267759ERRE-DE SRL02/08/2016InscriptoSociedad Responsabilidad Limitada2016340.01.379889e+085.0Buenos Aires2.01.0NaN1.03.0NaN1.02.02.01.0226.02
3530702024834Datastar Argentina S.A.09/09/2016InscriptoSociedad Anónima2016111.03.182254e+095.0Ciudad Autónoma de Buenos Aires2.01.0NaN1.02.02.02.02.04.0NaN207.02
3930623295946LAVIERI HNOS DE LAVIERI SEBASTIAN GABRIEL LAVIERI ALEJANDRO CARLOS.29/09/2016InscriptoSociedades De Hecho201622.07.831100e+065.0Ciudad Autónoma de Buenos Aires2.01.0NaN1.0NaN2.02.0NaN2.0NaN37.02
6030673249902LOMAS DEL SOL SRL21/10/2016SuspendidoSociedad Responsabilidad Limitada201627.01.375720e+095.0San Juan2.0NaN4.04.01.0NaN1.0NaN1.0NaN602.02
8530714236888NANOTEC S.R.L.30/09/2016InscriptoSociedad Responsabilidad Limitada201612.07.242921e+065.0Ciudad Autónoma de Buenos Aires2.01.0NaN1.01.0NaN1.0NaN1.0NaN160.02
8930710362218Licenciasonline SA13/09/2016InscriptoSociedad Anónima20161.08.607014e+075.0Ciudad Autónoma de Buenos Aires4.01.0NaN1.01.01.01.01.02.0NaN3.02
10330710828608INFORMÁTICA PALMAR SRL04/10/2016InscriptoSociedad Responsabilidad Limitada2016143.06.447596e+075.0Ciudad Autónoma de Buenos Aires2.01.0NaN1.0NaN2.01.01.02.0NaN37.02
CUITNombreFechaPreinscripcionEstadoTipoSocietarioanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveecluster_k_4
689420271824638Albano Equipamientos24/08/2018InscriptoPersona Física20183.02.859164e+063.0Buenos AiresNaN1.0NaN1.01.0NaN1.0NaN1.0NaN45.02
704630711958181DEL BIANCO ELECTRONICA S.A.13/03/2017InscriptoSociedad Anónima20173.02.405506e+054.0Ciudad Autónoma de Buenos Aires3.01.02.03.0NaN3.03.0NaN3.0NaN319.02
738730708351187AR TECHNOLOGY S.R.L.28/06/2018InscriptoSociedad Responsabilidad Limitada20185.08.783142e+063.0Ciudad Autónoma de Buenos Aires2.01.0NaN1.0NaN1.01.0NaN1.0NaN22.02
815030715995979DESARROLLOS URBANOS RIO DE LA PLATA SRL16/09/2020InscriptoSociedad Responsabilidad Limitada202018.02.095676e+081.0Ciudad Autónoma de Buenos Aires2.01.0NaN1.0NaN1.01.0NaN1.0NaN18.02
816530709585831SECON SECURITY CONCEPT SA11/04/2017InscriptoSociedad Anónima20171.01.326591e+074.0Buenos Aires2.01.0NaN1.01.01.01.01.01.01.0102.02
895627181287077FABIANA SANDRA CORTES10/04/2017InscriptoPersona Física201713.01.682270e+074.0Buenos AiresNaN1.07.08.01.0NaN1.0NaN1.0NaN254.02
8957A28006104AIRBUS DEFENCE AND SPACE S.A.21/06/2019Pre InscriptoPersona Jurídica Extranjero Sin Sucursal20193.08.494348e+042.0Extranjera1.01.0NaN1.02.0NaN2.0NaN2.0NaN14.02
9237214349010016FARINTO S.A.05/07/2019Pre InscriptoPersona Jurídica Extranjero Sin Sucursal2019NaNNaN2.0Extranjera1.01.0NaN1.01.0NaN1.0NaN1.0NaN6.02
950430716582082BATERIAS ECOBAT S.A.S16/10/2020InscriptoOtras Formas Societarias20201.08.288520e+051.0San Juan1.01.0NaN1.0NaN1.01.0NaN1.0NaN1.02
978330707835563SUTEL S.R.L.25/08/2016InscriptoSociedad Responsabilidad Limitada20162.03.889114e+065.0Buenos Aires2.0NaN2.02.0NaN1.01.0NaN1.0NaN329.02